"""simple docstring"""







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE             =logging.get_logger("transformers.models.speecht5")



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	Tuple	,     __SCREAMING_SNAKE_CASE				:	Any ):
     hf_model.apply_weight_norm()

     lowercase_      :							int						=      checkpoint['input_conv.weight_g']
     lowercase_      :							Tuple						=      checkpoint['input_conv.weight_v']
     lowercase_      :							Dict						=      checkpoint['input_conv.bias']

     for i in range(len(config.upsample_rates ) ):
          lowercase_      :							Union[str, Any]						=      checkpoint[F'''upsamples.{i}.1.weight_g''']
          lowercase_      :							List[Any]						=      checkpoint[F'''upsamples.{i}.1.weight_v''']
          lowercase_      :							int						=      checkpoint[F'''upsamples.{i}.1.bias''']

     for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
          for j in range(len(config.resblock_dilation_sizes ) ):
               lowercase_      :							int						=      checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
               lowercase_      :							Optional[Any]						=      checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
               lowercase_      :							str						=      checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

               lowercase_      :							str						=      checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
               lowercase_      :							List[str]						=      checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
               lowercase_      :							Any						=      checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

     lowercase_      :							Tuple						=      checkpoint['output_conv.1.weight_g']
     lowercase_      :							Tuple						=      checkpoint['output_conv.1.weight_v']
     lowercase_      :							str						=      checkpoint['output_conv.1.bias']

     hf_model.remove_weight_norm()



@torch.no_grad()
def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int]	,     __SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	str	,     __SCREAMING_SNAKE_CASE				:	Optional[Any]=None	,     __SCREAMING_SNAKE_CASE				:	Any=None	,     ):






     if config_path is not None:
          lowercase_      :							Union[str, Any]						=      SpeechTaHifiGanConfig.from_pretrained(a__ )
     else:
          lowercase_      :							List[str]						=      SpeechTaHifiGanConfig()

     lowercase_      :							str						=      SpeechTaHifiGan(a__ )

     lowercase_      :							List[Any]						=      torch.load(a__ )
     load_weights(orig_checkpoint['model']['generator']	,     a__	,     a__ )

     lowercase_      :							List[str]						=      np.load(a__ )
     lowercase_      :							List[Any]						=      stats[0].reshape(-1 )
     lowercase_      :							Optional[int]						=      stats[1].reshape(-1 )
     lowercase_      :							Union[str, Any]						=      torch.from_numpy(a__ ).float()
     lowercase_      :							Tuple						=      torch.from_numpy(a__ ).float()

     model.save_pretrained(a__ )

     if repo_id:
          print('Pushing to the hub...' )
          model.push_to_hub(a__ )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE             =argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
       parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
       )

       __SCREAMING_SNAKE_CASE             =parser.parse_args()
       convert_hifigan_checkpoint(
           args.checkpoint_path,
           args.stats_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )









"""simple docstring"""


import itertools
import math


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5     ,      int(math.sqrt(a__ ) + 1 )     ,      6 ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			2
						while True:
												if is_prime(a__ ):
																		yield num
												num += 1


def        UpperCAmelCase      (					a__ = 1_00_01 ):





						'''simple docstring'''






						return next(itertools.islice(prime_generator()     ,      nth - 1     ,      a__ ) )


if __name__ == "__main__":
		print(F"""{solution() = }""")